info:eu-repo/semantics/article
A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry
Fecha
2016-04-19Registro en:
0969-8043
Autor
Martínez Blanco, María del Rosario
Ornelas Vargas, Gerardo
Solís Sánchez, Luis Octavio
Castañeda Miranda, Rodrígo
Vega Carrillo, Héctor René
Celaya Padilla, José María
Garza Veloz, Idalia
Martínez Fierro, Margarita de la Luz
Ortíz Rodríguez, José Manuel
Institución
Resumen
The process of unfolding the neutron energy spectrum has been subject of research for many years.
Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the
methods used. The drawbacks associated with traditional unfolding procedures have motivated the research
of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied
with success in neutron spectrometry and dosimetry domains, however, the structure and learning
parameters are factors that highly impact in the networks performance. In ANN domain, Generalized
Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture
and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to
BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the
network development phase, the only hurdle is to optimize the hyper-parameter, which is known as
sigma, governing the smoothness of the network. The aim of this work was to compare the performance
of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be
observed that despite the very similar results, GRNN performs better than BPNN.